RT Journal Article
SR Electronic
T1 Personalized genetic assessment of age associated Alzheimer’s disease risk
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 074864
DO 10.1101/074864
A1 Rahul S. Desikan
A1 Chun Chieh Fan
A1 Yunpeng Wang
A1 Andrew J. Schork
A1 Howard J. Cabral
A1 L. Adrienne Cupples
A1 Wesley K. Thompson
A1 Lilah Besser
A1 Walter A. Kukull
A1 Dominic Holland
A1 Chi-Hua Chen
A1 James B. Brewer
A1 David S. Karow
A1 Karolina Kauppi
A1 Aree Witoelar
A1 Celeste M. Karch
A1 Luke W. Bonham
A1 Jennifer S. Yokoyama
A1 Howard J. Rosen
A1 Bruce L. Miller
A1 William P. Dillon
A1 David M. Wilson
A1 Christopher P. Hess
A1 Margaret Pericak-Vance
A1 Jonathan L. Haines
A1 Lindsay A. Farrer
A1 Richard Mayeux
A1 John Hardy
A1 Alison M. Goate
A1 Bradley T. Hyman
A1 Gerard D. Schellenberg
A1 Linda K. McEvoy
A1 Ole A. Andreassen
A1 Anders M. Dale
A1 for the ADNI and ADGC investigators
YR 2016
UL http://biorxiv.org/content/early/2016/09/13/074864.abstract
AB Importance Identifying individuals at risk for developing Alzheimer’s disease (AD) is of utmost importance. Although genetic studies have identified APOE and other AD associated single nucleotide polymorphisms (SNPs), genetic information has not been integrated into an epidemiological framework for personalized risk prediction.Objective To develop, replicate and validate a novel polygenic hazard score for predicting age-specific risk for AD.Setting Multi-center, multi-cohort genetic and clinical data.Participants We assessed genetic data from 17,008 AD patients and 37,154 controls from the International Genetics of Alzheimer’s Project (IGAP), and 6,409 AD patients and 9,386 older controls from Phase 1 Alzheimer’s Disease Genetics Consortium (ADGC). As independent replication and validation cohorts, we also evaluated genetic, neuroimaging, neuropathologic, CSF and clinical data from ADGC Phase 2, National Institute of Aging Alzheimer’s Disease Center (NIA ADC) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (total n = 20,680)Main Outcome(s) and Measure(s) Use the IGAP cohort to first identify AD associated SNPs (at p < 10-5). Next, integrate these AD associated SNPs into a Cox proportional hazards model using ADGC phase 1 genetic data, providing a polygenic hazard score (PHS) for each participant. Combine population based incidence rates, and genotype-derived PHS for each individual to derive estimates of instantaneous risk for developing AD, based on genotype and age. Finally, assess replication and validation of PHS in independent cohorts.Results Individuals in the highest PHS quantiles developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, PHS modified expected age of AD onset by more than 10 years between the lowest and highest deciles. In independent cohorts, PHS strongly predicted empirical age of AD onset (p = 1.1 x 10-26), longitudinal progression from normal aging to AD (p = 1.54 x 10-10) and associated with markers of AD neurodegeneration.Conclusions We developed, replicated and validated a clinically usable PHS for quantifying individual differences in age-specific risk of AD. Beyond APOE, polygenic architecture plays an important role in modifying AD risk. Precise quantification of AD genetic risk will be useful for early diagnosis and therapeutic strategies.